Implant Utilization and Time to Prosthetic Rehabilitation in Conventional and Advanced Fibular Free Flap Reconstruction of the Maxilla and Mandible
Bibliographic record
Abstract
PURPOSE: Precisely designed jaw reconstruction rehabilitation (JRR) is important to the integrity of the jaw structure and oral functions. Advanced three-dimensional (3D) digital surgical design and simulation (SDS) techniques have the potential to reduce time to reconstructive and dental treatment completion, thereby promoting early functional oral rehabilitation. This study investigated the use of SDS in JRR procedures. MATERIALS AND METHODS: A retrospective chart review was conducted on adult head and neck tumor (HNT) participants who completed JRR treatment with a fibular free flap (FFF) reconstruction. Two treatment approaches, advanced 3D SDS technique (with-SDS) and conventional, nondigitally planned technique (without-SDS), included the use of osseointegrated implants. Data were collected from adult patients treated between January 2000 and March 2014 at the Institute for Reconstructive Sciences in Medicine (iRSM). Participants were excluded if they underwent a bone-containing augmentation to the FFF reconstruction. The without-SDS group underwent a conventional, nonguided FFF reconstruction followed by nonguided implant placement. The with-SDS group underwent a guided FFF reconstruction with guided implant placement during the reconstructive surgery. The outcome measures included implant utilization (ratio of implants placed to connected) and time to prosthetic connection after FFF reconstruction. Mann-Whitney U test was used to analyze the data. RESULTS: The digital SDS technique (with-SDS) group completed prosthetic treatment with a significantly higher utilization of implants as well as a significantly shorter time to prosthetic delivery. CONCLUSION: SDS allows an interdisciplinary treatment team to work together to create a virtual plan that leads to greater efficiency in patient treatment time and utilization of dental implants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".